CN110935337A - Stirring system and stirring method - Google Patents
Stirring system and stirring method Download PDFInfo
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- CN110935337A CN110935337A CN201811105275.6A CN201811105275A CN110935337A CN 110935337 A CN110935337 A CN 110935337A CN 201811105275 A CN201811105275 A CN 201811105275A CN 110935337 A CN110935337 A CN 110935337A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
- B01F23/00—Mixing according to the phases to be mixed, e.g. dispersing or emulsifying
- B01F23/50—Mixing liquids with solids
- B01F23/51—Methods thereof
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
- B01F35/00—Accessories for mixers; Auxiliary operations or auxiliary devices; Parts or details of general application
- B01F35/20—Measuring; Control or regulation
- B01F35/21—Measuring
- B01F35/211—Measuring of the operational parameters
- B01F35/2113—Pressure
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
- B01F35/00—Accessories for mixers; Auxiliary operations or auxiliary devices; Parts or details of general application
- B01F35/20—Measuring; Control or regulation
- B01F35/21—Measuring
- B01F35/211—Measuring of the operational parameters
- B01F35/2115—Temperature
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
- B01F35/00—Accessories for mixers; Auxiliary operations or auxiliary devices; Parts or details of general application
- B01F35/20—Measuring; Control or regulation
- B01F35/21—Measuring
- B01F35/212—Measuring of the driving system data, e.g. torque, speed or power data
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
- B01F35/00—Accessories for mixers; Auxiliary operations or auxiliary devices; Parts or details of general application
- B01F35/20—Measuring; Control or regulation
- B01F35/21—Measuring
- B01F35/2135—Humidity, e.g. moisture content
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
- B01F35/00—Accessories for mixers; Auxiliary operations or auxiliary devices; Parts or details of general application
- B01F35/20—Measuring; Control or regulation
- B01F35/21—Measuring
- B01F35/2136—Viscosity
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
- B01F35/00—Accessories for mixers; Auxiliary operations or auxiliary devices; Parts or details of general application
- B01F35/20—Measuring; Control or regulation
- B01F35/22—Control or regulation
- B01F35/221—Control or regulation of operational parameters, e.g. level of material in the mixer, temperature or pressure
- B01F35/2214—Speed during the operation
- B01F35/22142—Speed of the mixing device during the operation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Abstract
The invention relates to a stirring system and a stirring method. The stirring system includes: a raw material charging device for charging the raw material to be stirred; a stirring device for mixing the charged stirring raw materials; a detection device for detecting the viscosity of the mixed stirring raw materials; and a control device for controlling at least one of the charging operation of the raw material charging device and the stirring operation of the stirring device, wherein the control device determines whether or not the change rate of the viscosity detected by the detection device is within a predetermined viscosity change rate determination range, and controls the charging operation or the stirring operation based on the determination result.
Description
Technical Field
The present invention relates to a stirring system used for manufacturing a large battery such as a lithium ion battery, and a stirring method for stirring by using the stirring system.
Background
In a production process of a product, a technique of mixing and stirring a solid and a liquid by a stirring device such as a stirrer is known. For example, in the production of large batteries such as lithium ion batteries, in order to facilitate handling of a powder material composed of an electrode active material such as graphite in the production processes of a negative electrode material, a positive electrode material, and the like, a stirring step of stirring and mixing the powder material into a solvent such as water or an organic solvent is required.
In recent years, with the widespread use of large batteries such as lithium ion batteries in the fields of hybrid vehicles, electric vehicles, and the like, there has been an increasing demand for a large capacity battery. As the capacity of batteries increases, the area of battery electrodes also increases, and in this case, the amount of electrode active material such as graphite used for the electrodes also increases, which requires the container capacity of the stirring device to be increased, and the size of the stirring rotor having a rod-like, plate-like, or propeller-like shape to be increased. Therefore, in the case of stirring a large amount of material, the stirring conditions become more complicated than in the case of stirring a small amount of material, and as a result, there is a problem that the stirred material is not sufficiently mixed into the solvent, that is, the mixture concentration is not uniform. If the mixture after agitation is in a state of uneven concentration, the quality of electrodes of a battery produced thereafter may be deviated, and in the worst case, a lithium ion battery may be short-circuited to cause ignition.
Conventionally, in order to determine the homogeneity of a powder mixture to be stirred, the following proposals have been made: a sample extracted from one or two or more different portions of a powder mixture is prepared as slurry water by water, the electrical conductivity of the slurry water is measured, and the uniformity of the mixed state of the powder mixture is determined based on the measured electrical conductivity (see, for example, patent document 1 below). Thus, the state of the stirred mixture can be determined based on the electrical conductivity.
Documents of the prior art
Patent document
Patent document 1: japanese patent laid-open No. 2015-141146
Disclosure of Invention
Technical problem
However, in the above patent document 1, since the state of the mixture is determined by measuring the electrical conductivity of the slurry prepared from one or two or more portions of the mixture after mixing, only the state of the mixture after stirring can be known, and the state of the mixture cannot be determined in real time during stirring.
Further, even if the sample is extracted and modulated during the stirring process to detect the electrical conductivity, if the determination result is that the state of the mixture is not uniform, it takes too long time to detect the feedback for instructing the stirring device to adjust, and particularly, it takes the most time to extract the sample-modulated slurry and measure the electrical conductivity. Therefore, when the stirring device takes measures to make adjustments, the state of the mixture has changed again, so that the homogeneity of the mixture cannot be improved with high precision.
The present invention has been made in view of the above problems, and an object of the present invention is to provide a stirring system and a stirring method capable of monitoring the state of a mixture in real time during stirring and taking measures in time to perform control with high accuracy to obtain a mixture with good uniformity.
Technical scheme for solving technical problem
In order to solve the above problem, a stirring system according to a first aspect of the present invention includes: a raw material charging device for charging a stirring raw material; a stirring device that mixes the charged stirring raw materials; a detection device that detects the viscosity of the mixed stirring material; and a control device that controls at least one of the charging operation of the raw material charging device and the stirring operation of the stirring device, wherein the control device determines whether or not the change rate of the viscosity detected by the detection device is within a predetermined viscosity change rate determination range, and controls the charging operation or the stirring operation based on the determination result.
In order to solve the above problem, a stirring method according to a second aspect of the present invention includes: a raw material charging step of charging a stirring raw material; a stirring step of mixing the charged stirring raw materials; a detection step of detecting the viscosity of the mixed stirring raw material; a determination step of determining whether or not the detected change rate of the viscosity is within a predetermined viscosity change rate determination range; and a control step of controlling the charging operation in the raw material charging step or the stirring operation in the stirring step based on a determination result.
In order to solve the above problem, a stirring system according to a third aspect of the present invention includes: a raw material charging device for charging a stirring raw material; a stirring device that mixes the charged stirring raw materials; a detection device that detects at least one state parameter of a stirring pressure and a stirring speed of a stirring rotor, a temperature and a humidity in the stirring device, and a mass of the stirring material charged by the material charging device when the stirring material is mixed by the stirring device; and a learning module that determines a viscosity change rate determination range of the viscosity of the mixed stirring material, the learning module including: a stirring condition learning module that learns a stirring condition that optimizes mixing of the stirring raw material based on the detected at least one state parameter of the stirring pressure, the stirring speed, the temperature, the humidity, and the quality of the stirring raw material; and a viscosity change rate determination range decision module that decides the viscosity change rate determination range under the learned agitation condition.
In order to solve the above problem, a stirring method according to a fourth aspect of the present invention includes: a raw material charging step of charging a stirring raw material; a stirring step of mixing the charged stirring raw materials; a detection step of detecting at least one state parameter of a stirring pressure and a stirring speed of a stirring rotor, a temperature and a humidity in the stirring apparatus, and a mass of the stirring raw material charged in the raw material charging step, when the stirring raw material is mixed by the stirring apparatus in the stirring step; and a learning step of determining a viscosity change rate determination range of the viscosity of the mixed stirring raw material, wherein in the learning step, a stirring condition for optimizing the mixing of the stirring raw material is learned based on at least one state parameter of the detected stirring pressure, stirring speed, temperature, humidity, and mass of the stirring raw material, and the viscosity change rate determination range under the learned stirring condition is determined.
Effects of the invention
According to the stirring system and the stirring method of the invention, the following effects are achieved: that is, the state of the mixture can be monitored in real time during the stirring, and the control can be performed with high accuracy to obtain a mixture having good uniformity.
Drawings
Fig. 1 is a block diagram showing a configuration of a stirring system according to embodiment 1 of the present invention.
Fig. 2 is a flowchart showing an example of the stirring method according to embodiment 1 of the present invention.
Fig. 3 is a graph showing the relationship between the viscosity of the stirring raw material and the stirring time according to embodiment 1 of the present invention.
Fig. 4 is a block diagram showing the configuration of a learning module according to embodiment 2 of the present invention.
Fig. 5 is a flowchart showing an example of a method of learning a viscosity change rate determination range according to embodiment 2 of the present invention.
Fig. 6 is a graph for explaining stirring conditions for preparing four samples according to an example of embodiment 2 of the present invention.
Fig. 7 is a schematic diagram for explaining the height ratio of the agitation vessel for preparing the four samples shown in fig. 6.
Fig. 8 is a schematic diagram for explaining uniformity of the concentration of the stirring raw material in the stirring device.
Fig. 9 is a graph showing the relationship between the height ratio and the solid concentration of the four-sample stirring vessel according to the example of embodiment 2 of the present invention.
Fig. 10 is a graph showing the rate of change in the viscosity of the stirred raw material of the four samples with time, corresponding to the concentration uniformity shown in fig. 9.
Fig. 11 is a diagram showing an example of different stirring conditions of the stirring system according to embodiment 2 of the present invention.
FIG. 12 is a graph showing the viscosity change rate judgment range under each stirring condition of FIG. 11.
Detailed Description
Next, a stirring device and a stirring method according to an embodiment of the present invention will be described with reference to the drawings. The stirring system and the stirring method of the present invention are used for stirring and mixing a negative electrode material or a positive electrode material when manufacturing a large-sized battery such as a lithium ion battery, for example.
Fig. 1 is a block diagram showing the configuration of a stirring system according to embodiment 1. As shown in fig. 1, the stirring system includes a raw material charging device 1, a stirring device 2, a detection device 3, and a control device 4.
The raw material charging device 1 is used to charge a stirring raw material such as a positive electrode active material (for example, lithium), a negative electrode active material (for example, graphite), a binder, a dispersion solvent (for example, water or an organic solvent), and a viscosity adjuster (for example, a dispersion thickener) into the stirring device 2.
The stirring device 2 mixes the stirred raw materials fed from the raw material feeding device 1. The stirring device 2 includes one or more cylindrical stirring vessels, and a stirring rotor driven by, for example, a motor or the like is disposed in the stirring vessel. The current and voltage input to the motor are controlled to rotate the stirring rotor at a constant rotational speed and torque, thereby mixing and stirring the stirring material charged into the stirring vessel.
The detection device 3 is, for example, various sensors provided in the raw material charging device 1, the stirring device 2, and the like, and detects a state parameter of stirring. As shown in fig. 1, the state parameters include, for example, but not limited to, the viscosity of the mixed material to be stirred, the stirring pressure and the stirring speed of the stirring rotor when the stirring device 2 mixes the material to be stirred, parameters such as the temperature and humidity in the stirring vessel of the stirring device 2 and in the stirring environment, and parameters such as the mass of the material to be stirred which is charged by the material charging device 1. For example, the condition parameters may include parameters such as the concentration, particle size, and density of the mixed stirring raw material, and parameters such as the density of the stirring raw material charged by the raw material charging device 1. As a method of detecting the viscosity of the stirring material, for example, the force applied to the stirring rotor of the stirring device 2 may be detected, or the torque applied to the stirring rotor of the stirring device 2 may be detected and then converted to obtain the viscosity of the stirring material.
The control device 4 controls at least one of the charging operation of the material charging device 1 and the stirring operation of the stirring device 2, and includes a learning module 41, a determination module 42, a notification module 43, a state parameter acquisition module 44, a reward parameter acquisition module 45, a control module 46, a material charging control module 47, and a stirring control module 48.
The learning module 41 is connected to the detection device 3 via the state parameter acquisition module 44, learns the stirring conditions for optimizing the mixing of the stirring material based on at least one state parameter of the stirring pressure, the stirring speed, the temperature, the humidity, and the quality of the stirring material from the detection device 3 acquired by the state parameter acquisition module 44, and determines a viscosity change rate determination range, which is a determination criterion for controlling the charging operation of the material charging device 1 or the stirring operation of the stirring device 2, to be described later, based on the learned stirring conditions. Further, the specific structure of the learning module 41 and the specific method of learning the agitation condition will be described in detail below.
The determination module 42 is connected to the detection device 3 via the state parameter acquisition module 44, acquires the determined viscosity change rate determination range from the learning module 41, and determines the stirring state in the stirring device 2 based on whether or not the change rate of the viscosity from the detection device 3 acquired by the state parameter acquisition module 44 is within the viscosity change rate determination range.
The notification module 43 is connected to the determination module 42, and notifies the other device 6 of the stirring state determined by the determination module 42. The other device 6 is, for example, an external device such as a display, a speaker, and an alarm connected to the mixing system, or another device in the latter stage of the mixing system. When the determination module 42 determines that the stirring state is abnormal, the notification module 43 notifies the other device 6 of the abnormal stirring state.
As shown in fig. 1, the parameter setting module 5 may receive input from an operator, receive incentive parameters extracted from a production management system (e.g., quality management database, etc.), or receive incentive parameters extracted from a technical file. The reward parameter acquiring module 45 is connected to the parameter setting module 5, and receives the parameter from the parameter setting module 5, and the parameter is used as the reward parameter in the learning module 41. The learning module 41 acquires the incentive parameter via the incentive parameter acquiring module 45, calculates an incentive based on the incentive parameter, and uses the calculated incentive for learning of the agitation condition. In the embodiment of the present invention, the reward parameter is the uniformity of the concentration of the mixed agitation material, and the learning module 41 calculates the reward based on the uniformity of the concentration of the agitation material. For example, if the concentration uniformity of the agitation material is high, the reward is increased (for example, a reward of "1" is given). On the other hand, if the uniformity of the concentration of the agitation material is low, the reward is reduced (e.g., a reward of "-1" is given). In addition to the uniformity of the concentration, the reward parameter may be, for example, a level at which the mixed material after completion of stirring reaches a predetermined viscosity, or a stirring time taken to stir the raw material to reach a predetermined viscosity.
The control module 46 receives the determination result of the stirring state from the determination module 42, and when an abnormality occurs in the stirring state, sends a control signal to each of the raw material charge control module 47 and the stirring control module 48. The raw material charging control module 47, upon receiving a control signal from the control module 46, transmits control data to the raw material charging device 1 to control the charging operation so that the viscosity adjusting agent for mixing to a predetermined viscosity is charged. On the other hand, the agitation control module 48, upon receiving the control signal from the control module 46, transmits control data to the agitation device 2 to control the agitation operation so as to shorten or lengthen the operation time of the agitation rotor.
The stirring method according to embodiment 1 will be explained below. The stirring method is mainly suitable for the stirring process in the production stage.
Fig. 2 is a flowchart showing an example of the stirring method according to embodiment 1 of the present invention. As shown in fig. 2, first, in step S101, a raw material is charged into the stirring vessel of the stirring apparatus 2 from the raw material charging apparatus 1. Next, in step S102, the control device 4 determines whether or not the stirring device 2 is in a state in which stirring can be started. If it is determined that the stirring is started (yes in step S102), the process proceeds to step S103, and the stirring apparatus 2 starts stirring. When it is determined that stirring cannot be started (no in step S102), the flow is terminated as it is. Returning to step S103, after the stirring by the stirring device 2 is started, the process proceeds to step S104, and the detection device 3 starts to detect the viscosity of the stirred material. In step S105, the state parameter acquisition module 44 receives the viscosity of the stirred raw material as the state parameter from the detection device 3, removes noise of the detection value using a filter element or the like, and sends the filtered detection value to the determination module 42. In step S106, the determination module 42 calculates the rate of change of the viscosity based on the received detection value. In step S107, the determination module 42 determines whether or not the calculated viscosity change rate is within a predetermined viscosity change rate determination range acquired from the learning module 41. When it is determined that the viscosity change rate is within the predetermined viscosity change rate determination range (yes in step S107), the process proceeds to step S111, and the control device 4 then determines whether or not the stirring end condition is satisfied. The stirring termination condition may be, for example, a condition that the viscosity of the kneaded material reaches a predetermined value after the stirring, or a condition that a predetermined time has elapsed after the stirring is started. If it is determined that the termination condition is not satisfied (no in step S111), the flow returns to step S104, the viscosity of the stirring raw material is continuously detected, and the processing from step S104 to step S107 is repeatedly performed. When it is determined that the termination condition is satisfied (yes in step S111), the process proceeds to step S110, where the stirring by the stirring device 2 is stopped, and the flow is terminated. On the other hand, when it is determined in step S107 that the viscosity change rate is out of the viscosity change rate determination range (NO in step S107), the process proceeds to step S108, and the notification module 43 notifies the other device 6 of the abnormality. Then, in step S109, whether or not to continue stirring is determined, for example, based on an input from the operator. When it is determined that the stirring is to be continued (yes in step S109), the process proceeds to step S112, and the control module 46 sends a control signal to the raw material charging control module 47 and/or the stirring control module 48, and further sends control data to the raw material charging apparatus 1 to control the charging operation, or sends control data to the stirring apparatus 2 to control the stirring operation. Then, the flow returns to step S104, the viscosity of the stirred material is continuously detected, and the processing from step S104 to step S107 is repeatedly performed. If it is determined in step S109 that the stirring is not to be continued (no in step S109), the process proceeds to step S110, where the stirring is stopped by the stirring device 2, and the flow ends. In other words, the detection device 3 repeatedly detects the viscosity of the stirred material at predetermined time intervals, and performs control until the operator instructs that stirring is not continued or the condition for completion of stirring is satisfied when the viscosity change rate is out of the viscosity change rate determination range.
Next, the technical effects of the stirring system and the stirring method according to embodiment 1 will be described with reference to the graph of fig. 3.
Fig. 3 is a graph showing the relationship between the viscosity of the stirring raw material and the stirring time in embodiment 1. In fig. 3, four solid curves (a) to (d) show the viscosity change rate in different stirring states, respectively, and the region between the two dotted curves shows the viscosity change rate determination range predetermined by the learning module 41 of the control device 4.
As shown in fig. 3, when the rotational speed of the stirring rotor is gradually increased at the start of stirring, if the rotational speed of the stirring rotor is erroneously set, for example, to be set to a rotational speed that is higher than a normal value, the viscosity change rate may be out of the viscosity change rate determination range as shown in the graph (a). Therefore, by determining whether or not the viscosity change rate for a predetermined time after starting stirring is out of the viscosity change rate determination range, it can be determined that the stirring state is abnormal in the rotational speed setting of the stirring rotor as described above when the viscosity change rate is out of the viscosity change rate determination range.
Further, as shown in fig. 3, the rotational speed of the stirring rotor tends to be stable during the stirring, and at this time, if the stirring rotor is suddenly damaged, the viscosity change rate may deviate from the viscosity change rate determination range as shown in the curve (b). Therefore, by determining whether or not the viscosity change rate is out of the viscosity change rate determination range at predetermined time intervals after the start of stirring, it can be determined that the stirring rotor breakage abnormality as described above has occurred in the stirring state when the out-of-viscosity is generated.
On the other hand, as shown in fig. 3, when the viscosity change rate is always within the viscosity change rate determination range as in the case of the solid curves (c) and (d), it can be determined that no abnormality has occurred in the stirring state.
According to the configuration of embodiment 1, it is determined whether or not the change rate of the viscosity detected by the detection device is within the predetermined viscosity change rate determination range, and the charging operation or the stirring operation is controlled based on the determination result, so that the state of the mixture can be monitored in real time during the stirring, and the control can be performed with high accuracy to obtain a mixture with good uniformity.
Further, since it is determined whether or not the change rate of the viscosity at a predetermined time after the start of stirring is out of the viscosity change rate determination range, it is possible to detect an abnormality caused by the rotation speed of the stirring rotor being set to be much higher than a normal value at the start of stirring.
Further, since it is determined whether or not the change rate of the viscosity is out of the viscosity change rate determination range at predetermined time intervals after the start of the stirring, it is possible to detect an abnormality caused by the sudden breakage of the stirring rotor during the stirring.
Further, when the determination module determines that the stirring state is abnormal, the stirring operation is controlled to shorten or lengthen the operation time of the stirring rotor, or the feeding operation is controlled to feed the viscosity adjusting agent for mixing to a predetermined viscosity, so that the viscosity change rate can be adjusted in time by adjusting the operation time of the stirring rotor or feeding the viscosity adjusting agent after the abnormality occurs, and the viscosity change rate can be returned to the viscosity change rate determination range as soon as possible.
Next, a specific configuration of the learning module 41 in embodiment 1 will be described in detail. The learning module 41 of embodiment 2 learns the mixing conditions for optimizing the mixing of the mixing material by a machine learning method.
The monitoring device 3 monitors, as the state parameters, at least one of the stirring pressure and the stirring speed of the stirring rotor, the temperature and humidity in the stirring device, and the quality of the stirred material charged by the material charging device when the stirring device mixes the stirred material.
The learning module 41 learns the mixing conditions that optimize the mixing of the mixing material, based on a training data set generated based on at least one state parameter of the mixing pressure and the mixing speed of the mixing rotor, the temperature and humidity in the mixing device, and the mass of the mixing material that is input by the material input device when the mixing device mixes the mixing material.
The learning module 41 may use any learning algorithm. As an example, a case where Reinforcement Learning (Reinforcement Learning) is applied will be described. Reinforcement learning refers to an agent (agent) in a certain environment observing the current state and determining an action to be taken. The agent receives rewards from the environment by selecting actions and learns the countermeasures most rewarded by a series of actions. As representative methods of reinforcement learning, Q-learning (Q-learning) and TD-learning (time difference learning) are known. For example, in the case of Q learning, a general update (action value table) of the action value function Q (s, a) is represented by equation 1.
[ mathematical formula 1]
In mathematical formula 1, stIndicates the state at time t, atIndicating the action at time t. Due to action atThe state is changed to st+1。rt+1Indicates the reward obtained by the change of the state, γ indicates the discount rate, α indicates the learning coefficient, and when Q learning is applied, the mixing condition for optimizing the mixing of the mixing material becomes action at。
In the update shown in equation 1, if the behavior value of the optimal action a at time t +1 is greater than the action value Q of the action a executed at time t, the action value Q at time t is increased, and conversely, the action value Q at time t is decreased. In other words, the action a at the time ttThe action value function Q (s, a) is updated so that the action value Q(s) approaches the optimum action value at the time t + 1. Thus, the best action value in a certain environment is propagated to the action values in its previous environment in turn.
The learning module 41 also includes a reward calculation module 403 and a cost function calculation module 404.
The reward calculation module 403 calculates a reward r based on the uniformity of the concentration of the blending material. For example, if the uniformity of the concentration of the agitation material is high, the payout r is increased (e.g., a payout of "1"). On the other hand, if the uniformity of the concentration of the agitation material is low, the payout r is reduced (e.g., a payout of "-1" is given). Further, the calculation of the reward r may also be calculated in consideration of the stirring time. The reward criteria are extracted according to known methods.
The function updating unit updates the function for determining the stirring condition (the stirring condition for optimizing the mixing of the stirring raw materials) in accordance with the reward calculated by the reward calculation unit. For example, in the case of Q learning, the action merit function Q(s) expressed by equation 1 is usedt,at) As for stirringAs a function of the stirring conditions for optimum mixing of the raw materials.
Fig. 4 is a block diagram showing the configuration of a learning module according to embodiment 2 of the present invention. As shown in fig. 4, the learning module 41 includes a stirring condition learning module 401 and a viscosity change rate determination range determining module 402.
The stirring condition learning module 401 is composed of a reward calculation module 403, a cost function calculation module 404, and a stirring condition update module 405. The reward calculation module 403 acquires the reward parameter from the reward parameter acquisition module 45, and in embodiment 2, the concentration of the mixed agitation material is used as the reward parameter. In embodiment 2, the operator measures the concentration of the mixed agitation raw material, and the parameter setting module 5 inputs the concentration as the reward parameter to the reward parameter acquisition module 45. However, the method of acquiring the reward parameters is not limited to this, and for example, the detection device may directly detect the concentration of the mixed stirring raw materials and input the detection result to the reward parameter acquiring module 45. The reward calculation module 403 calculates a reward based on the received reward parameters. In embodiment 2, the uniformity of the concentration of the mixed stirring raw materials is used as a reward, and the calculation method thereof will be described in detail below. The calculated award is input to the cost function calculation module 404 together with the state parameters of the stirring pressure, the stirring speed, the temperature, the humidity, the quality of the stirred material, the viscosity, etc. acquired by the state parameter acquisition module 44. Then, the merit function calculation module 404 calculates and updates the value using the action value table or the approximation function prepared in advance as described above, obtains the optimum action (stirring condition) based on the reward (density uniformity), and outputs the action (stirring condition) to the stirring condition storage unit 49 via the stirring condition update module 405.
The viscosity change rate determination range determination module 402 obtains the learned optimal stirring condition via the stirring condition update module 405, and determines the viscosity change rate determination range under the optimal stirring condition.
The stirring condition storage unit 49 and the viscosity change rate determination range storage unit 50 store the optimum stirring condition and the viscosity change rate determination range corresponding thereto, respectively, for use in the subsequent stirring operation and abnormality determination in the mass production stage.
Next, a specific method of learning the stirring conditions will be described in detail. The method is suitable for learning the stirring conditions in the production preparation stage before mass production.
Fig. 5 is a flowchart showing an example of the method of learning the viscosity change rate determination range according to embodiment 2. As shown in fig. 5, first, in step S201, stirring conditions are initialized. The stirring conditions include, for example, items such as stirring intensity and temperature, and the number and pressure of the stirring rotor, which can be specified by the stirring device, items such as humidity in the stirring device, which can be specified by the dehumidifier, and items such as the weight and density of the dispersion thickener and the dispersion solvent (for example, water and organic solvent), which can be specified by the raw material charging device. Next, in step S202, the stirring raw material is charged into the stirring apparatus 2 from the raw material charging apparatus 1, and the process proceeds to step S203, where the stirring apparatus 2 is started to mix the charged stirring raw material. Then, in step S204, while stirring, the detection device 3 detects at least one state parameter of, for example, the stirring pressure and the stirring speed of the stirring rotor when the stirring device 2 mixes the stirring raw material during stirring, the temperature and the humidity in the stirring device 2, and the mass of the stirring raw material charged by the raw material charging device 1, and detects the change rate of the viscosity of the stirring raw material during stirring. After the stirring for a certain period of time has elapsed, the process proceeds to step S205, and the stirring apparatus 2 stops the stirring. Thereafter, in step S206, the operator detects the concentration of the mixed material after the stirring, and inputs the detected concentration as the reward parameter to the reward parameter acquisition module 45 through the parameter setting module 5. Then, in step S207, the reward calculation module 403 that acquires the concentration from the reward parameter acquisition module 45 calculates the concentration uniformity, outputs the calculated concentration uniformity as a reward to the merit function calculation module 404, and proceeds to step S208. In step S208, the cost function calculation module 404 updates the cost function based on the obtained concentration uniformity and the state parameter obtained from the detection device 3 by the state parameter obtaining module 44, and stores the current stirring condition and the change curve of the viscosity change rate under the current stirring condition in step S209 thereafter. Thereafter, in step S210, the stirring condition updating module 405 updates the stirring conditions based on the state parameters detected in step S204, and then proceeds to step S210. In step S210, it is determined whether or not the agitation condition learning is completed. If it is determined that the learning has not been completed (no in step S210), the process proceeds to step S214, the mixed material after the current stirring is discharged, the process returns to step S202, new stirring materials are newly charged, and the stirring and learning are started under the updated stirring conditions. On the other hand, when it is determined that the learning is completed (yes in step S210), the process proceeds to step S212, where the optimized stirring condition is acquired from the cost function, and the acquired optimized stirring condition is stored in the stirring condition storage unit 49 and used as the stirring condition in the subsequent mass production stage. Finally, the process proceeds to step S213, where the range of viscosity change rates corresponding to the optimized stirring conditions is stored as a viscosity change rate determination range in the viscosity change rate determination range storage unit 50, and the process is terminated.
The embodiment of performing machine learning by reinforcement learning is described, but machine learning may be performed by other known methods such as a neural network, a genetic algorithm, a Support Vector Machine (SVM), and the like.
An embodiment in which a stirring condition for optimizing the mixing of the stirring material is learned based on at least one state parameter and a viscosity change rate determination range under the stirring condition is determined will be described below with reference to the drawings.
Fig. 6 is a graph for explaining stirring conditions for preparing four samples according to an example of embodiment 2. As shown in fig. 6, samples 1 to 4 are prepared by steps S202 to S205, respectively, under stirring conditions of, for example, the rotation speed (rpm) of the stirring rotor, the temperature in the stirring apparatus, and the humidity in the stirring apparatus. Then, the concentration uniformity of the samples 1 to 4 after the stirring is calculated in steps S206 to S207, respectively.
Fig. 7 is a schematic diagram for explaining the height ratio of the stirring vessel for preparing the four samples shown in fig. 6, fig. 8 is a schematic diagram for explaining the uniformity of the concentration of the stirring raw material in the stirring apparatus, fig. 9 is a graph showing the relationship between the height ratio and the solid concentration of the stirring vessel for the four samples according to the present example, and fig. 10 is a graph showing the rate of change in the viscosity of the stirring raw material for the four samples with time corresponding to the uniformity of the concentration shown in fig. 9. As shown in fig. 7, the height ratios of the cylindrical stirring vessels are designated as 0, 0.2, 0.4, and … … 1.0.0, and the solid concentration (unit: wt%) of each sample after stirring at each height ratio is measured, thereby obtaining a graph shown in fig. 9. As can be seen from the graph of fig. 9, the difference in solid concentration between the bottom of the stirring device and the upper part of the stirring device of the sample 1 is large, and the state is as shown in the left side of fig. 8, and therefore, the mixing state is poor, that is, the reward (concentration uniformity) of the sample 1 is small. In contrast, the difference in the solid concentration between the bottom of the stirring device and the upper portion of the stirring device of the sample 4 is small, and the state thereof is as shown in the right side of fig. 8, and therefore, the mixing state thereof is good, that is, the reward (concentration uniformity) of the sample 4 is large. As shown in fig. 10, the graphs of the viscosity change rates of the samples 1 to 4 having different concentration uniformity are different from each other. Therefore, by detecting the viscosity change rate under each stirring condition (each sample), the concentration uniformity of each sample after stirring can be indirectly known. Fig. 10 shows only one viscosity change rate curve corresponding to one stirring condition, but actually, the viscosity change rate under the same stirring condition may fluctuate within a range, and thus one viscosity change rate determination range may be formed. Here, the viscosity change rate means a change amount of the viscosity of the sample per unit time, that is, a speed of change of the viscosity.
Next, the difference in the viscosity change rate determination range according to embodiment 2 due to the difference in the stirring conditions will be described with reference to the drawings.
Fig. 11 is a diagram showing an example of different stirring conditions of the stirring system according to embodiment 2. FIG. 12 is a graph showing the viscosity change rate judgment range under each stirring condition of FIG. 11. As shown in FIG. 11, the stirring rotor speeds under the stirring conditions A1 and A2 were 60 rpm, the humidity was 0.5 <, and the difference between them was that the temperature under the stirring conditions A1 was 27 ℃ and the temperature under the stirring conditions A2 was 7 ℃. As is clear from fig. 12, since the obtained viscosity change rate determination ranges are different when the temperature in the stirring apparatus is different, the viscosity change rate determination range under the optimized stirring conditions can be determined by obtaining the optimized stirring conditions through learning.
According to the configuration of embodiment 2, since the stirring conditions for optimizing the mixing of the stirring material are learned based on at least one state parameter of the stirring pressure and the stirring speed of the stirring rotor when the stirring material is mixed by the stirring device, the temperature and the humidity in the stirring device, and the quality of the stirring material charged by the material charging device, and the viscosity change rate determination range under the learned stirring conditions is determined, even when the stirring vessel has a different shape, the stirring environment has a different shape, or the like, the optimum reward, the concentration uniformity of the mixed material after stirring, or the like can be obtained by learning the optimized stirring conditions in advance, and it is possible to determine whether or not the viscosity change rate of the stirring material is within the viscosity change rate determination range corresponding to the optimized stirring conditions by detecting and determining in the subsequent mass production process, the state of the mixture is monitored in real time, so that the mixture with good uniformity can be obtained by controlling with high precision.
The embodiments of the present invention and examples thereof have been described above. It should be understood that the embodiments and examples disclosed herein are illustrative only and not intended to be limiting in all respects. The scope of the present invention is indicated by the appended claims, rather than by the foregoing embodiments and examples, and all changes and modifications that come within the meaning and range of equivalency of the claims are intended to be embraced therein.
Industrial applicability of the invention
As described above, the stirring system and the stirring method according to the present invention are useful for a process for preparing positive and negative electrode materials in the manufacture of large-sized batteries such as lithium ion batteries.
Description of the reference symbols
1 raw material charging device
2 stirring device
3 detection device
4 control device
5 parameter setting module
6 other devices
41 learning module
42 decision module
43 Notification Module
44 status parameter acquisition module
45 reward parameter acquisition module
46 control module
47 raw material input control module
48 stirring control module
49 stirring condition storage section
50 viscosity change rate determination range storage unit
401 stirring condition learning module
402 viscosity change rate judgment range determining module
403 reward calculation module
404 cost function calculation module
405 stirring condition updating module
Claims (20)
1. A blending system, comprising:
a raw material charging device for charging a stirring raw material;
a stirring device that mixes the charged stirring raw materials;
a detection device that detects the viscosity of the mixed stirring material; and
a control device for controlling at least one of the charging operation of the raw material charging device and the stirring operation of the stirring device,
the control device determines whether or not the change rate of the viscosity detected by the detection device is within a predetermined viscosity change rate determination range, and controls the charging operation or the stirring operation based on the determination result.
2. The mixing system of claim 1,
the detection device detects at least one state parameter of a stirring pressure and a stirring speed of the stirring rotor, a temperature and humidity in the stirring device, and a mass of the stirring material charged by the material charging device when the stirring material is mixed by the stirring device,
the control device includes:
a learning module that learns a stirring condition that optimizes mixing of the stirring material based on the detected at least one state parameter of the stirring pressure, the stirring speed, the temperature, the humidity, and the quality of the stirring material, and determines the viscosity change rate determination range based on the learned stirring condition; and
a determination module that determines a stirring state in the stirring device based on whether or not the change rate of the viscosity detected by the detection device is within the viscosity change rate determination range determined by the learning module.
3. The mixing system of claim 2,
the determination module determines whether or not a change rate of the viscosity for a predetermined time after starting stirring is out of the viscosity change rate determination range, and determines that the stirring state is abnormal when the change rate of the viscosity is out of the viscosity change rate determination range.
4. The mixing system of claim 2,
the determination module determines whether or not the change rate of the viscosity is out of the viscosity change rate determination range at predetermined time intervals after the start of stirring, and determines that the stirring state is abnormal when the change rate of the viscosity is out of the viscosity change rate determination range.
5. Stirring system according to claim 3 or 4,
the control device controls the stirring operation to shorten or lengthen the operation time of the stirring rotor or controls the feeding operation to feed a viscosity modifier for mixing to a predetermined viscosity when the determination module determines that the stirring state is abnormal.
6. Stirring system according to claim 3 or 4,
the control device includes a notification module that notifies the other devices of the agitation state,
the notification module notifies the other device of the abnormal stirring state, when the determination module determines that the abnormal stirring state occurs.
7. The stirring system of any one of claims 1 to 4,
the detection device detects the viscosity of the stirring material based on a force applied to a stirring rotor of the stirring device.
8. The stirring system of any one of claims 1 to 4,
the detection device detects the viscosity of the stirring material based on a torque applied to a stirring rotor of the stirring device.
9. The stirring system of any one of claims 1 to 4,
and the stirred raw material is used as a negative electrode material or a positive electrode material of the lithium ion battery.
10. A method of stirring, comprising:
a raw material charging step of charging a stirring raw material;
a stirring step of mixing the charged stirring raw materials;
a detection step of detecting the viscosity of the mixed stirring raw material;
a determination step of determining whether or not the detected change rate of the viscosity is within a predetermined viscosity change rate determination range; and
and a control step of controlling the charging operation in the raw material charging step or the stirring operation in the stirring step based on a determination result.
11. The stirring method according to claim 10,
the detecting step detects at least one state parameter of a stirring pressure and a stirring speed of a stirring rotor when the stirring raw material is mixed by a stirring device in the stirring step, a temperature and humidity in the stirring device, and a mass of the stirring raw material charged in the raw material charging step,
the stirring method further includes a learning step of learning a stirring condition that optimizes mixing of the stirring raw material based on the detected at least one state parameter of the stirring pressure, the stirring speed, the temperature, the humidity, and the quality of the stirring raw material, and determining the viscosity change rate determination range based on the learned stirring condition,
the determining step determines the stirring state in the stirring device based on whether or not the detected change rate of the viscosity is within the viscosity change rate determination range.
12. The stirring method according to claim 11,
in the determining step, it is determined whether or not a change rate of the viscosity for a predetermined time after starting stirring is out of the viscosity change rate determination range, and it is determined that the stirring state is abnormal when the change rate of the viscosity is out of the viscosity change rate determination range.
13. The stirring method according to claim 11,
in the determining step, it is determined whether or not the change rate of the viscosity is out of the viscosity change rate determination range at predetermined time intervals after the start of stirring, and it is determined that the stirring state is abnormal when the change rate of the viscosity is out of the viscosity change rate determination range.
14. The stirring method according to claim 12 or 13,
in the control step, when it is determined that the stirring state is abnormal, the stirring operation is controlled to shorten or lengthen the operation time of the stirring rotor, or the feeding operation is controlled to feed a viscosity modifier for mixing to a predetermined viscosity.
15. The stirring method according to claim 12 or 13,
further comprising a notification step of notifying, when it is determined that the agitation state is abnormal, another device of the abnormality in the agitation state.
16. The stirring method according to any one of claims 10 to 13,
in the detecting step, the viscosity of the stirred material is detected based on a force applied to a stirring rotor of the stirring device.
17. The stirring method according to any one of claims 10 to 13,
in the detecting step, the viscosity of the stirring material is detected based on a torque applied to a stirring rotor of the stirring device.
18. The stirring method according to any one of claims 10 to 13,
and the stirred raw material is used as a negative electrode material or a positive electrode material of the lithium ion battery.
19. A blending system, comprising:
a raw material charging device for charging a stirring raw material;
a stirring device that mixes the charged stirring raw materials;
a detection device that detects at least one state parameter of a stirring pressure and a stirring speed of a stirring rotor, a temperature and a humidity in the stirring device, and a mass of the stirring material charged by the material charging device when the stirring material is mixed by the stirring device; and
a learning module that determines a viscosity change rate determination range of the viscosity of the mixed stirring raw material,
the learning module includes:
a stirring condition learning module that learns a stirring condition that optimizes mixing of the stirring raw material based on the detected at least one state parameter of the stirring pressure, the stirring speed, the temperature, the humidity, and the quality of the stirring raw material; and
a viscosity change rate determination range decision module that decides the viscosity change rate determination range under the learned agitation condition.
20. A method of stirring, comprising:
a raw material charging step of charging a stirring raw material;
a stirring step of mixing the charged stirring raw materials;
a detection step of detecting at least one state parameter of a stirring pressure and a stirring speed of a stirring rotor, a temperature and a humidity in the stirring apparatus, and a mass of the stirring raw material charged in the raw material charging step, when the stirring raw material is mixed by the stirring apparatus in the stirring step; and
a learning step of determining a viscosity change rate determination range of the viscosity of the mixed stirring raw material,
in the step of learning, the learning step is performed,
learning a stirring condition that optimizes mixing of the stirring raw material based on the detected at least one state parameter of the stirring pressure, the stirring speed, the temperature, the humidity, and the mass of the stirring raw material,
determining the viscosity change rate determination range under the learned stirring condition.
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| CN114834713A (en) * | 2022-04-27 | 2022-08-02 | 江苏苏嘉集团新材料有限公司 | Intelligent steelmaking magnesia carbon brick raw material preparation system and raw material preparation production line thereof |
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